Analysis of GLDS-213 from NASA GeneLab

This R markdown file was auto-generated by the iDEP website Using iDEP 0.91, originally by Steven

Ge SX, Son EW, Yao R: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018, 19(1):534. PMID:30567491

1. Read data

First we set up the working directory to where the files are saved.

 setwd('~/Documents/HTML_R/GLDS213')

R packages and iDEP core Functions. Users can also download the iDEP_core_functions.R file. Many R packages needs to be installed first. This may take hours. Each of these packages took years to develop.So be a patient thief. Sometimes dependencies needs to be installed manually. If you are using an older version of R, and having trouble with package installation, try un-install the current version of R, delete all folders and files (C:/Program Files/R/R-3.4.3), and reinstall from scratch.

 if(file.exists('iDEP_core_functions.R'))
    source('iDEP_core_functions.R') else 
    source('https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/iDEP_core_functions.R')

We are using the downloaded gene expression file where gene IDs has been converted to Ensembl gene IDs. This is because the ID conversion database is too large to download. You can use your original file if your file uses Ensembl ID, or you do not want to use the pathway files available in iDEP (or it is not available).

 inputFile <- 'GLDS213_Expression.csv'
 sampleInfoFile <- 'GLDS213_Sampleinfo.csv'
 gldsMetadataFile <- 'GLDS213_Metadata.csv'
 geneInfoFile <- 'Arabidopsis_thaliana__athaliana_eg_gene_GeneInfo.csv' #Gene symbols, location etc. 
 geneSetFile <- 'Arabidopsis_thaliana__athaliana_eg_gene.db'  # pathway database in SQL; can be GMT format 
 STRING10_speciesFile <- 'https://raw.githubusercontent.com/iDEP-SDSU/idep/master/shinyapps/idep/STRING10_species.csv'

Parameters for reading data

 input_missingValue <- 'geneMedian' #Missing values imputation method
 input_dataFileFormat <- 1  #1- read counts, 2 FKPM/RPKM or DNA microarray
 input_minCounts <- 0.5 #Min counts
 input_NminSamples <- 1 #Minimum number of samples 
 input_countsLogStart <- 4  #Pseudo count for log CPM
 input_CountsTransform <- 1 #Methods for data transformation of counts. 1-EdgeR's logCPM 2-VST, 3-rlog 
readMetadata.out <- readMetadata(gldsMetadataFile)
library(knitr)   #  install if needed. for showing tables with kable
library(kableExtra)
kable( readMetadata.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
FLT_Cen_Rep1 FLT_Cen_Rep2 FLT_uG_Rep1 FLT_uG_Rep2 GC_1G_Rep1 GC_1G_Rep2
Sample.LongId Atha.Col.0.clsCC.FLT.1G.Rep1.Array Atha.Col.0.clsCC.FLT.1G.Rep2.Array Atha.Col.0.clsCC.FLT.uG.Rep1.Array Atha.Col.0.clsCC.FLT.uG.Rep2.Array Atha.Col.0.clsCC.GC.1G.Rep1.Array Atha.Col.0.clsCC.GC.1G.Rep2.Array
Sample.Id Atha.Col.0.clsCC.FLT.1G.Rep1 Atha.Col.0.clsCC.FLT.1G.Rep2 Atha.Col.0.clsCC.FLT.uG.Rep1 Atha.Col.0.clsCC.FLT.uG.Rep2 Atha.Col.0.clsCC.GC.1G.Rep1 Atha.Col.0.clsCC.GC.1G.Rep2
Sample.Name Atha_Col-0_clsCC_FLT_1G_Rep1 Atha_Col-0_clsCC_FLT_1G_Rep2 Atha_Col-0_clsCC_FLT_uG_Rep1 Atha_Col-0_clsCC_FLT_uG_Rep2 Atha_Col-0_clsCC_GC_1G_Rep1 Atha_Col-0_clsCC_GC_1G_Rep2
GLDS 213 213 213 213 213 213
Accession GLDS-213 GLDS-213 GLDS-213 GLDS-213 GLDS-213 GLDS-213
Hardware SIMBOX centrafuge vs GC SIMBOX centrafuge vs GC SIMBOX centrafuge vs GC SIMBOX centrafuge vs GC SIMBOX centrafuge vs GC SIMBOX centrafuge vs GC
Tissue Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures Cell cultures
Age 16 days 16 days 16 days 16 days 16 days 16 days
Organism Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana Arabidopsis thaliana
Ecotype Col-0 Col-0 Col-0 Col-0 Col-0 Col-0
Genotype WT WT WT WT WT WT
Variety Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT Col-0 WT
Radiation Cosmic radiation Cosmic radiation Cosmic radiation Cosmic radiation Background Earth Background Earth
Gravity Microgravity with 1G centrafuge Microgravity with 1G centrafuge Microgravity Microgravity Terrestrial Terrestrial
Developmental 16 day old cell culture 16 day old cell culture 16 day old cell culture 16 day old cell culture 16 day old cell culture 16 day old cell culture
Time.series.or.Concentration.gradient Single time point Single time point Single time point Single time point Single time point Single time point
Light White light White light White light White light White light White light
Assay..RNAseq. Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling Microarray Transcription Profiling
Temperature 22-24 22-24 22-24 22-24 22-24 22-24
Treatment.type A WholeGenome Microarray Study of Arabidopsis thaliana Semisolid Callus Cultures Exposed to Microgravity and Nonmicrogravity Related Spaceflight Conditions for 5 on Board of Shenzhou 8 A WholeGenome Microarray Study of Arabidopsis thaliana Semisolid Callus Cultures Exposed to Microgravity and Nonmicrogravity Related Spaceflight Conditions for 5 on Board of Shenzhou 8 A WholeGenome Microarray Study of Arabidopsis thaliana Semisolid Callus Cultures Exposed to Microgravity and Nonmicrogravity Related Spaceflight Conditions for 5 on Board of Shenzhou 8 A WholeGenome Microarray Study of Arabidopsis thaliana Semisolid Callus Cultures Exposed to Microgravity and Nonmicrogravity Related Spaceflight Conditions for 5 on Board of Shenzhou 8 A WholeGenome Microarray Study of Arabidopsis thaliana Semisolid Callus Cultures Exposed to Microgravity and Nonmicrogravity Related Spaceflight Conditions for 5 on Board of Shenzhou 8 A WholeGenome Microarray Study of Arabidopsis thaliana Semisolid Callus Cultures Exposed to Microgravity and Nonmicrogravity Related Spaceflight Conditions for 5 on Board of Shenzhou 8
Treatment.intensity x x x x x x
Treament.timing x x x x x x
Preservation.Method. RNAlater RNAlater RNAlater RNAlater RNAlater RNAlater 
 readData.out <- readData(inputFile) 
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
   kable( head(readData.out$data) ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
FLT_Cen_Rep1 FLT_Cen_Rep2 FLT_uG_Rep1 FLT_uG_Rep2 GC_1G_Rep1 GC_1G_Rep2
AT1G03850 3.321928 3.321928 3.321928 3.321928 3.906891 3.906891
AT5G43580 3.169925 3.000000 3.169925 3.169925 3.807355 3.807355
AT1G30700 3.000000 3.000000 3.169925 3.169925 3.807355 3.700440
AT3G02480 3.000000 3.169925 3.000000 3.000000 3.807355 3.584963
AT5G22270 3.169925 3.169925 3.459432 3.459432 3.906891 3.807355
AT3G01420 3.169925 3.000000 3.169925 3.169925 3.700440 3.584963 
 readSampleInfo.out <- readSampleInfo(sampleInfoFile) 
 kable( readSampleInfo.out ) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Gravity
FLT_Cen_Rep1 Centrafuge
FLT_Cen_Rep2 Centrafuge
FLT_uG_Rep1 Microgravity
FLT_uG_Rep2 Microgravity
GC_1G_Rep1 Terrestrial
GC_1G_Rep2 Terrestrial 
 input_selectOrg ="NEW" 
 input_selectGO <- 'GOBP'   #Gene set category 
 input_noIDConversion = TRUE  
 allGeneInfo.out <- geneInfo(geneInfoFile) 
 converted.out = NULL 
 convertedData.out <- convertedData()    
 nGenesFilter()  
## [1] "16156 genes in 6 samples. 16156  genes passed filter.\n Original gene IDs used."
 convertedCounts.out <- convertedCounts()  # converted counts, just for compatibility

2. Pre-process

# Read counts per library 
 parDefault = par() 
 par(mar=c(12,4,2,2)) 
 # barplot of total read counts
 x <- readData.out$rawCounts
 groups = as.factor( detectGroups(colnames(x ) ) )
 if(nlevels(groups)<=1 | nlevels(groups) >20 )  
  col1 = 'green'  else
  col1 = rainbow(nlevels(groups))[ groups ]             
         
 barplot( colSums(x)/1e6, 
        col=col1,las=3, main="Total read counts (millions)")  

 readCountsBias()  # detecting bias in sequencing depth 
## [1] 0.2359756
## [1] 0.2359756
## [1] "No bias detected"
 # Box plot 
 x = readData.out$data 
 boxplot(x, las = 2, col=col1,
    ylab='Transformed expression levels',
    main='Distribution of transformed data') 

 #Density plot 
 par(parDefault) 
## Warning in par(parDefault): graphical parameter "cin" cannot be set
## Warning in par(parDefault): graphical parameter "cra" cannot be set
## Warning in par(parDefault): graphical parameter "csi" cannot be set
## Warning in par(parDefault): graphical parameter "cxy" cannot be set
## Warning in par(parDefault): graphical parameter "din" cannot be set
## Warning in par(parDefault): graphical parameter "page" cannot be set
 densityPlot()       

 # Scatter plot of the first two samples 
 plot(x[,1:2],xlab=colnames(x)[1],ylab=colnames(x)[2], 
    main='Scatter plot of first two samples') 

 ####plot gene or gene family
 input_selectOrg ="BestMatch" 
 input_geneSearch <- 'HOXA' #Gene ID for searching 
 genePlot()  
## NULL
 input_useSD <- 'FALSE' #Use standard deviation instead of standard error in error bar? 
 geneBarPlotError()       
## NULL

3. Heatmap

 # hierarchical clustering tree
 x <- readData.out$data
 maxGene <- apply(x,1,max)
 # remove bottom 25% lowly expressed genes, which inflate the PPC
 x <- x[which(maxGene > quantile(maxGene)[1] ) ,] 
 plot(as.dendrogram(hclust2( dist2(t(x)))), ylab="1 - Pearson C.C.", type = "rectangle") 

 #Correlation matrix
 input_labelPCC <- TRUE #Show correlation coefficient? 
 correlationMatrix() 

 # Parameters for heatmap
 input_nGenes <- 1000   #Top genes for heatmap
 input_geneCentering <- TRUE    #centering genes ?
 input_sampleCentering <- FALSE #Center by sample?
 input_geneNormalize <- FALSE   #Normalize by gene?
 input_sampleNormalize <- FALSE #Normalize by sample?
 input_noSampleClustering <- FALSE  #Use original sample order
 input_heatmapCutoff <- 4   #Remove outliers beyond number of SDs 
 input_distFunctions <- 1   #which distant funciton to use
 input_hclustFunctions <- 1 #Linkage type
 input_heatColors1 <- 1 #Colors
 input_selectFactorsHeatmap <- NULL     #Sample coloring factors 
 png('heatmap.png', width = 10, height = 15, units = 'in', res = 300) 
 staticHeatmap() 
 dev.off()  
## png 
##   2

[heatmap] (heatmap.png)

 heatmapPlotly() # interactive heatmap using Plotly

4. K-means clustering

 input_nGenesKNN <- 2000    #Number of genes fro k-Means
 input_nClusters <- 4   #Number of clusters 
 maxGeneClustering = 12000
 input_kmeansNormalization <- 'geneMean'    #Normalization
 input_KmeansReRun <- 0 #Random seed 

 distributionSD()  #Distribution of standard deviations 

 KmeansNclusters()  #Number of clusters 

 Kmeans.out = Kmeans()   #Running K-means 
 KmeansHeatmap()   #Heatmap for k-Means 

 #Read gene sets for enrichment analysis 
 sqlite  <- dbDriver('SQLite')
 input_selectGO3 <- NULL    #Gene set category
 input_minSetSize <- 15 #Min gene set size
 input_maxSetSize <- 2000   #Max gene set size 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO3,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 # Alternatively, users can use their own GMT files by
 #GeneSets.out <- readGMTRobust('somefile.GMT')  
 results <- KmeansGO()  #Enrichment analysis for k-Means clusters   
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.81e-53 70 Photosynthesis
1.22e-44 162 Response to abiotic stimulus
1.51e-32 137 Response to organic substance
1.33e-31 122 Response to hormone
1.42e-31 40 Photosynthesis, light reaction
4.65e-31 122 Response to endogenous stimulus
1.39e-29 76 Response to light stimulus
1.78e-28 76 Response to radiation
1.08e-26 108 Cellular response to chemical stimulus
2.05e-23 101 Oxidation-reduction process
B 1.08e-09 17 Regulation of cell cycle process
1.16e-07 19 Regulation of organelle organization
1.86e-07 43 Oxidation-reduction process
8.83e-07 14 Regulation of mitotic cell cycle
8.83e-07 11 Regulation of nuclear division
1.07e-06 10 Regulation of mitotic nuclear division
1.09e-06 18 Regulation of cell cycle
2.09e-06 52 Phosphate-containing compound metabolic process
3.22e-06 52 Phosphorus metabolic process
3.22e-06 20 Regulation of cellular component organization
C 5.41e-28 60 Response to external stimulus
1.26e-27 51 Response to external biotic stimulus
1.26e-27 51 Response to other organism
1.81e-27 51 Response to biotic stimulus
1.94e-25 52 Defense response
4.73e-22 52 Multi-organism process
2.05e-19 52 Cellular response to chemical stimulus
4.83e-18 26 Response to fungus
2.46e-17 35 Defense response to other organism
3.99e-15 55 Response to abiotic stimulus
D 1.07e-27 124 Response to abiotic stimulus
4.30e-22 109 Response to organic substance
5.44e-21 94 Response to oxygen-containing compound
5.90e-19 33 Cellular response to decreased oxygen levels
5.90e-19 33 Cellular response to oxygen levels
5.90e-19 33 Cellular response to hypoxia
1.11e-18 81 Response to external stimulus
1.98e-18 86 Cellular response to chemical stimulus
1.52e-17 33 Response to hypoxia
2.22e-17 33 Response to decreased oxygen levels 
 input_seedTSNE <- 0    #Random seed for t-SNE
 input_colorGenes <- TRUE   #Color genes in t-SNE plot? 
 tSNEgenePlot()  #Plot genes using t-SNE

5. PCA and beyond

 input_selectFactors <- 'Gravity'   #Factor coded by color
 input_selectFactors2 <- 'Sample_Name'  #Factor coded by shape
 input_tsneSeed2 <- 0   #Random seed for t-SNE 
 #PCA, MDS and t-SNE plots
 PCAplot()  

 MDSplot() 

 tSNEplot()  

 #Read gene sets for pathway analysis using PGSEA on principal components 
 input_selectGO6 <- 'GOBP' 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO6,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 PCApathway() # Run PGSEA analysis 
## Warning: Package 'KEGG.db' is deprecated and will be removed from Bioconductor
##   version 3.12

 cat( PCA2factor() )   #The correlation between PCs with factors 
## 
##  Correlation between Principal Components (PCs) with factors
## PC1 is correlated with Gravity (p=1.48e-02).

6. DEG1

 input_CountsDEGMethod <- 2 #DESeq2= 3,limma-voom=2,limma-trend=1 
 input_limmaPval <- 0.1 #FDR cutoff
 input_limmaFC <- 2 #Fold-change cutoff
 input_selectModelComprions <- c('Gravity: Terrestrial vs. Centrafuge','Gravity: Terrestrial vs. Microgravity') #Selected comparisons
 input_selectFactorsModel <- 'Gravity'  #Selected comparisons
 input_selectInteractions <- NULL   #Selected comparisons
 input_selectBlockFactorsModel <- NULL  #Selected comparisons
 factorReferenceLevels.out <- c('Gravity:Terrestrial') 

 limma.out <- limma()
## Error in if (treatments[kp] == treatments[kk]) {: missing value where TRUE/FALSE needed
 DEG.data.out <- DEG.data()
## Error in DEG.data(): object 'limma.out' not found
 limma.out$comparisons 
## Error in eval(expr, envir, enclos): object 'limma.out' not found
 input_selectComparisonsVenn = limma.out$comparisons[1:3] # use first three comparisons
## Error in eval(expr, envir, enclos): object 'limma.out' not found
 input_UpDownRegulated <- FALSE #Split up and down regulated genes 
 vennPlot() # Venn diagram 
## Error in vennPlot(): object 'limma.out' not found
  sigGeneStats() # number of DEGs as figure 
## Error in sigGeneStats(): object 'limma.out' not found
  sigGeneStatsTable() # number of DEGs as table 
## Error in sigGeneStatsTable(): object 'limma.out' not found

7. DEG2

 input_selectContrast = limma.out$comparisons[1] # use first  comparisons 
## Error in eval(expr, envir, enclos): object 'limma.out' not found
 selectedHeatmap.data.out <- selectedHeatmap.data()
## Error in selectedHeatmap.data(): object 'limma.out' not found
 selectedHeatmap()   # heatmap for DEGs in selected comparison
## Error in selectedHeatmap(): object 'selectedHeatmap.data.out' not found
 # Save gene lists and data into files
 write.csv( selectedHeatmap.data()$genes, 'heatmap.data.csv') 
## Error in selectedHeatmap.data(): object 'limma.out' not found
 write.csv(DEG.data(),'DEG.data.csv' )
## Error in DEG.data(): object 'limma.out' not found
 write(AllGeneListsGMT() ,'AllGeneListsGMT.gmt')
## Error in AllGeneListsGMT(): object 'limma.out' not found
 input_selectGO2 <- 'GOBP'  #Gene set category 
 geneListData.out <- geneListData()  
## Error in geneListData(): object 'input_selectContrast' not found
 volcanoPlot()  
## Error in volcanoPlot(): object 'limma.out' not found
  scatterPlot()  
## Error in scatterPlot(): object 'limma.out' not found
  MAplot()  
## Error in MAplot(): object 'limma.out' not found
  geneListGOTable.out <- geneListGOTable()  
## Error in geneListGOTable(): object 'selectedHeatmap.data.out' not found
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO2,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_removeRedudantSets <- TRUE   #Remove highly redundant gene sets? 
 results <- geneListGO()  #Enrichment analysis
## Error in geneListGO(): object 'geneListGOTable.out' not found
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.81e-53 70 Photosynthesis
1.22e-44 162 Response to abiotic stimulus
1.51e-32 137 Response to organic substance
1.33e-31 122 Response to hormone
1.42e-31 40 Photosynthesis, light reaction
4.65e-31 122 Response to endogenous stimulus
1.39e-29 76 Response to light stimulus
1.78e-28 76 Response to radiation
1.08e-26 108 Cellular response to chemical stimulus
2.05e-23 101 Oxidation-reduction process
B 1.08e-09 17 Regulation of cell cycle process
1.16e-07 19 Regulation of organelle organization
1.86e-07 43 Oxidation-reduction process
8.83e-07 14 Regulation of mitotic cell cycle
8.83e-07 11 Regulation of nuclear division
1.07e-06 10 Regulation of mitotic nuclear division
1.09e-06 18 Regulation of cell cycle
2.09e-06 52 Phosphate-containing compound metabolic process
3.22e-06 52 Phosphorus metabolic process
3.22e-06 20 Regulation of cellular component organization
C 5.41e-28 60 Response to external stimulus
1.26e-27 51 Response to external biotic stimulus
1.26e-27 51 Response to other organism
1.81e-27 51 Response to biotic stimulus
1.94e-25 52 Defense response
4.73e-22 52 Multi-organism process
2.05e-19 52 Cellular response to chemical stimulus
4.83e-18 26 Response to fungus
2.46e-17 35 Defense response to other organism
3.99e-15 55 Response to abiotic stimulus
D 1.07e-27 124 Response to abiotic stimulus
4.30e-22 109 Response to organic substance
5.44e-21 94 Response to oxygen-containing compound
5.90e-19 33 Cellular response to decreased oxygen levels
5.90e-19 33 Cellular response to oxygen levels
5.90e-19 33 Cellular response to hypoxia
1.11e-18 81 Response to external stimulus
1.98e-18 86 Cellular response to chemical stimulus
1.52e-17 33 Response to hypoxia
2.22e-17 33 Response to decreased oxygen levels

STRING-db API access. We need to find the taxonomy id of your species, this used by STRING. First we try to guess the ID based on iDEP’s database. Users can also skip this step and assign NCBI taxonomy id directly by findTaxonomyID.out = 10090 # mouse 10090, human 9606 etc.

 STRING10_species = read.csv(STRING10_speciesFile)  
 ix = grep('Arabidopsis thaliana', STRING10_species$official_name ) 
 findTaxonomyID.out <- STRING10_species[ix,1] # find taxonomyID
 findTaxonomyID.out  
## [1] 3702

Enrichment analysis using STRING

  STRINGdb_geneList.out <- STRINGdb_geneList() #convert gene lists
## Error in STRINGdb_geneList(): object 'geneListData.out' not found
 input_STRINGdbGO <- 'Process'  #'Process', 'Component', 'Function', 'KEGG', 'Pfam', 'InterPro' 
 results <- stringDB_GO_enrichmentData()  # enrichment using STRING 
## Error in stringDB_GO_enrichmentData(): object 'selectedHeatmap.data.out' not found
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.81e-53 70 Photosynthesis
1.22e-44 162 Response to abiotic stimulus
1.51e-32 137 Response to organic substance
1.33e-31 122 Response to hormone
1.42e-31 40 Photosynthesis, light reaction
4.65e-31 122 Response to endogenous stimulus
1.39e-29 76 Response to light stimulus
1.78e-28 76 Response to radiation
1.08e-26 108 Cellular response to chemical stimulus
2.05e-23 101 Oxidation-reduction process
B 1.08e-09 17 Regulation of cell cycle process
1.16e-07 19 Regulation of organelle organization
1.86e-07 43 Oxidation-reduction process
8.83e-07 14 Regulation of mitotic cell cycle
8.83e-07 11 Regulation of nuclear division
1.07e-06 10 Regulation of mitotic nuclear division
1.09e-06 18 Regulation of cell cycle
2.09e-06 52 Phosphate-containing compound metabolic process
3.22e-06 52 Phosphorus metabolic process
3.22e-06 20 Regulation of cellular component organization
C 5.41e-28 60 Response to external stimulus
1.26e-27 51 Response to external biotic stimulus
1.26e-27 51 Response to other organism
1.81e-27 51 Response to biotic stimulus
1.94e-25 52 Defense response
4.73e-22 52 Multi-organism process
2.05e-19 52 Cellular response to chemical stimulus
4.83e-18 26 Response to fungus
2.46e-17 35 Defense response to other organism
3.99e-15 55 Response to abiotic stimulus
D 1.07e-27 124 Response to abiotic stimulus
4.30e-22 109 Response to organic substance
5.44e-21 94 Response to oxygen-containing compound
5.90e-19 33 Cellular response to decreased oxygen levels
5.90e-19 33 Cellular response to oxygen levels
5.90e-19 33 Cellular response to hypoxia
1.11e-18 81 Response to external stimulus
1.98e-18 86 Cellular response to chemical stimulus
1.52e-17 33 Response to hypoxia
2.22e-17 33 Response to decreased oxygen levels

PPI network retrieval and analysis

 input_nGenesPPI <- 100 #Number of top genes for PPI retrieval and analysis 
 stringDB_network1(1) #Show PPI network 
## Error in stringDB_network1(1): object 'STRINGdb_geneList.out' not found

Generating interactive PPI

 write(stringDB_network_link(), 'PPI_results.html') # write results to html file 
## Error in stringDB_network_link(): object 'STRINGdb_geneList.out' not found
 browseURL('PPI_results.html') # open in browser

8. Pathway analysis

 input_selectContrast1 = limma.out$comparisons[1] 
## Error in eval(expr, envir, enclos): object 'limma.out' not found
 #input_selectContrast1 = limma.out$comparisons[3] # manually set
 input_selectGO <- 'GOBP'   #Gene set category 
 #input_selectGO='custom' # if custom gmt file
 input_minSetSize <- 15 #Min size for gene set
 input_maxSetSize <- 2000   #Max size for gene set 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_pathwayPvalCutoff <- 0.2 #FDR cutoff
 input_nPathwayShow <- 30   #Top pathways to show
 input_absoluteFold <- FALSE    #Use absolute values of fold-change?
 input_GenePvalCutoff <- 1  #FDR to remove genes 

 input_pathwayMethod = 1  # 1  GAGE
 gagePathwayData.out <- gagePathwayData()  # pathway analysis using GAGE  
## Error in gagePathwayData(): object 'limma.out' not found
 results <- gagePathwayData.out  #Enrichment analysis for k-Means clusters  
## Error in eval(expr, envir, enclos): object 'gagePathwayData.out' not found
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.81e-53 70 Photosynthesis
1.22e-44 162 Response to abiotic stimulus
1.51e-32 137 Response to organic substance
1.33e-31 122 Response to hormone
1.42e-31 40 Photosynthesis, light reaction
4.65e-31 122 Response to endogenous stimulus
1.39e-29 76 Response to light stimulus
1.78e-28 76 Response to radiation
1.08e-26 108 Cellular response to chemical stimulus
2.05e-23 101 Oxidation-reduction process
B 1.08e-09 17 Regulation of cell cycle process
1.16e-07 19 Regulation of organelle organization
1.86e-07 43 Oxidation-reduction process
8.83e-07 14 Regulation of mitotic cell cycle
8.83e-07 11 Regulation of nuclear division
1.07e-06 10 Regulation of mitotic nuclear division
1.09e-06 18 Regulation of cell cycle
2.09e-06 52 Phosphate-containing compound metabolic process
3.22e-06 52 Phosphorus metabolic process
3.22e-06 20 Regulation of cellular component organization
C 5.41e-28 60 Response to external stimulus
1.26e-27 51 Response to external biotic stimulus
1.26e-27 51 Response to other organism
1.81e-27 51 Response to biotic stimulus
1.94e-25 52 Defense response
4.73e-22 52 Multi-organism process
2.05e-19 52 Cellular response to chemical stimulus
4.83e-18 26 Response to fungus
2.46e-17 35 Defense response to other organism
3.99e-15 55 Response to abiotic stimulus
D 1.07e-27 124 Response to abiotic stimulus
4.30e-22 109 Response to organic substance
5.44e-21 94 Response to oxygen-containing compound
5.90e-19 33 Cellular response to decreased oxygen levels
5.90e-19 33 Cellular response to oxygen levels
5.90e-19 33 Cellular response to hypoxia
1.11e-18 81 Response to external stimulus
1.98e-18 86 Cellular response to chemical stimulus
1.52e-17 33 Response to hypoxia
2.22e-17 33 Response to decreased oxygen levels 
 pathwayListData.out = pathwayListData() 
## Error in pathwayListData(): object 'gagePathwayData.out' not found
 enrichmentPlot(pathwayListData.out, 25  ) 
## Error in enrichmentPlot(pathwayListData.out, 25): object 'pathwayListData.out' not found
  enrichmentNetwork(pathwayListData.out )  
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
  enrichmentNetworkPlotly(pathwayListData.out) 
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
 input_pathwayMethod = 3  # 1  fgsea 
 fgseaPathwayData.out <- fgseaPathwayData() #Pathway analysis using fgsea 
## Error in fgseaPathwayData(): object 'limma.out' not found
 results <- fgseaPathwayData.out  #Enrichment analysis for k-Means clusters 
## Error in eval(expr, envir, enclos): object 'fgseaPathwayData.out' not found
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.81e-53 70 Photosynthesis
1.22e-44 162 Response to abiotic stimulus
1.51e-32 137 Response to organic substance
1.33e-31 122 Response to hormone
1.42e-31 40 Photosynthesis, light reaction
4.65e-31 122 Response to endogenous stimulus
1.39e-29 76 Response to light stimulus
1.78e-28 76 Response to radiation
1.08e-26 108 Cellular response to chemical stimulus
2.05e-23 101 Oxidation-reduction process
B 1.08e-09 17 Regulation of cell cycle process
1.16e-07 19 Regulation of organelle organization
1.86e-07 43 Oxidation-reduction process
8.83e-07 14 Regulation of mitotic cell cycle
8.83e-07 11 Regulation of nuclear division
1.07e-06 10 Regulation of mitotic nuclear division
1.09e-06 18 Regulation of cell cycle
2.09e-06 52 Phosphate-containing compound metabolic process
3.22e-06 52 Phosphorus metabolic process
3.22e-06 20 Regulation of cellular component organization
C 5.41e-28 60 Response to external stimulus
1.26e-27 51 Response to external biotic stimulus
1.26e-27 51 Response to other organism
1.81e-27 51 Response to biotic stimulus
1.94e-25 52 Defense response
4.73e-22 52 Multi-organism process
2.05e-19 52 Cellular response to chemical stimulus
4.83e-18 26 Response to fungus
2.46e-17 35 Defense response to other organism
3.99e-15 55 Response to abiotic stimulus
D 1.07e-27 124 Response to abiotic stimulus
4.30e-22 109 Response to organic substance
5.44e-21 94 Response to oxygen-containing compound
5.90e-19 33 Cellular response to decreased oxygen levels
5.90e-19 33 Cellular response to oxygen levels
5.90e-19 33 Cellular response to hypoxia
1.11e-18 81 Response to external stimulus
1.98e-18 86 Cellular response to chemical stimulus
1.52e-17 33 Response to hypoxia
2.22e-17 33 Response to decreased oxygen levels 
  pathwayListData.out = pathwayListData() 
## Error in pathwayListData(): object 'fgseaPathwayData.out' not found
 enrichmentPlot(pathwayListData.out, 25  ) 
## Error in enrichmentPlot(pathwayListData.out, 25): object 'pathwayListData.out' not found
  enrichmentNetwork(pathwayListData.out )  
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
  enrichmentNetworkPlotly(pathwayListData.out) 
## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'pathwayListData.out' not found
   PGSEAplot() # pathway analysis using PGSEA 
## Error in PGSEAplot(): object 'input_selectContrast1' not found

9. Chromosome

 input_selectContrast2 = limma.out$comparisons[1] 
## Error in eval(expr, envir, enclos): object 'limma.out' not found
 #input_selectContrast2 = limma.out$comparisons[3] # manually set
 input_limmaPvalViz <- 0.1  #FDR to filter genes
 input_limmaFCViz <- 2  #FDR to filter genes 
 genomePlotly() # shows fold-changes on the genome 
## Error in genomePlotly(): object 'limma.out' not found

10. Biclustering

 input_nGenesBiclust <- 1000    #Top genes for biclustering
 input_biclustMethod <- 'BCCC()'    #Method: 'BCCC', 'QUBIC', 'runibic' ... 
 biclustering.out = biclustering()  # run analysis

 input_selectBicluster <- NULL  #select a cluster 
 biclustHeatmap()   # heatmap for selected cluster 
## Error in res[[i]] <- x[BicRes@RowxNumber[, number[i]], BicRes@NumberxCol[number[i], : attempt to select less than one element in integerOneIndex
 input_selectGO4 = 'GOBP'  # gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO4,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  )  
 results <- geneListBclustGO()  #Enrichment analysis for k-Means clusters   
## Error in res[[i]] <- x[BicRes@RowxNumber[, number[i]], BicRes@NumberxCol[number[i], : attempt to select less than one element in integerOneIndex
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.81e-53 70 Photosynthesis
1.22e-44 162 Response to abiotic stimulus
1.51e-32 137 Response to organic substance
1.33e-31 122 Response to hormone
1.42e-31 40 Photosynthesis, light reaction
4.65e-31 122 Response to endogenous stimulus
1.39e-29 76 Response to light stimulus
1.78e-28 76 Response to radiation
1.08e-26 108 Cellular response to chemical stimulus
2.05e-23 101 Oxidation-reduction process
B 1.08e-09 17 Regulation of cell cycle process
1.16e-07 19 Regulation of organelle organization
1.86e-07 43 Oxidation-reduction process
8.83e-07 14 Regulation of mitotic cell cycle
8.83e-07 11 Regulation of nuclear division
1.07e-06 10 Regulation of mitotic nuclear division
1.09e-06 18 Regulation of cell cycle
2.09e-06 52 Phosphate-containing compound metabolic process
3.22e-06 52 Phosphorus metabolic process
3.22e-06 20 Regulation of cellular component organization
C 5.41e-28 60 Response to external stimulus
1.26e-27 51 Response to external biotic stimulus
1.26e-27 51 Response to other organism
1.81e-27 51 Response to biotic stimulus
1.94e-25 52 Defense response
4.73e-22 52 Multi-organism process
2.05e-19 52 Cellular response to chemical stimulus
4.83e-18 26 Response to fungus
2.46e-17 35 Defense response to other organism
3.99e-15 55 Response to abiotic stimulus
D 1.07e-27 124 Response to abiotic stimulus
4.30e-22 109 Response to organic substance
5.44e-21 94 Response to oxygen-containing compound
5.90e-19 33 Cellular response to decreased oxygen levels
5.90e-19 33 Cellular response to oxygen levels
5.90e-19 33 Cellular response to hypoxia
1.11e-18 81 Response to external stimulus
1.98e-18 86 Cellular response to chemical stimulus
1.52e-17 33 Response to hypoxia
2.22e-17 33 Response to decreased oxygen levels

11. Co-expression network

 input_mySoftPower <- 5 #SoftPower to cutoff
 input_nGenesNetwork <- 1000    #Number of top genes
 input_minModuleSize <- 20  #Module size minimum 
 wgcna.out = wgcna()   # run WGCNA  
## Warning: executing %dopar% sequentially: no parallel backend registered
##    Power SFT.R.sq   slope truncated.R.sq mean.k. median.k. max.k.
## 1      1  0.82200  2.5600         0.8610     660     703.0    798
## 2      2  0.75400  1.1800         0.7730     504     543.0    685
## 3      3  0.53100  0.7050         0.4960     410     440.0    608
## 4      4  0.28100  0.3800         0.1800     345     365.0    550
## 5      5  0.12700  0.1780         0.0748     298     308.0    506
## 6      6  0.00908  0.0511        -0.1480     263     262.0    470
## 7      7  0.00713 -0.0421        -0.2080     235     226.0    441
## 8      8  0.05010 -0.1280        -0.1030     212     196.0    417
## 9      9  0.13500 -0.2330         0.0346     194     171.0    396
## 10    10  0.19600 -0.4600        -0.0208     178     149.0    378
## 11    12  0.25500 -0.5080         0.0518     154     116.0    348
## 12    14  0.32500 -0.6820         0.2000     137      92.6    324
## 13    16  0.34100 -0.7250         0.2060     123      75.8    304
## 14    18  0.36200 -0.8710         0.2060     112      63.0    288
## 15    20  0.35300 -0.9030         0.2260     104      53.3    274
## TOM calculation: adjacency..
## ..will not use multithreading.
##  Fraction of slow calculations: 0.000000
## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.
 softPower()  # soft power curve 

  modulePlot()  # plot modules  

  listWGCNA.Modules.out = listWGCNA.Modules() #modules
 input_selectGO5 = 'GOBP'  # gene set category 
 # Read pathway data again 
 GeneSets.out <-readGeneSets( geneSetFile,
    convertedData.out, input_selectGO5,input_selectOrg,
    c(input_minSetSize, input_maxSetSize)  ) 
 input_selectWGCNA.Module <- NULL   #Select a module
 input_topGenesNetwork <- 10    #SoftPower to cutoff
 input_edgeThreshold <- 0.4 #Number of top genes 
 moduleNetwork()    # show network of top genes in selected module
## Error in strsplit(input_selectWGCNA.Module, " "): non-character argument
 input_removeRedudantSets <- TRUE   #Remove redundant gene sets 
 results <- networkModuleGO()  #Enrichment analysis of selected module
## Error in strsplit(input_selectWGCNA.Module, " "): non-character argument
 results$adj.Pval <- format( results$adj.Pval,digits=3 )
 kable( results, row.names=FALSE) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  scroll_box(width = "100%")
Cluster adj.Pval Genes Pathways
A 1.81e-53 70 Photosynthesis
1.22e-44 162 Response to abiotic stimulus
1.51e-32 137 Response to organic substance
1.33e-31 122 Response to hormone
1.42e-31 40 Photosynthesis, light reaction
4.65e-31 122 Response to endogenous stimulus
1.39e-29 76 Response to light stimulus
1.78e-28 76 Response to radiation
1.08e-26 108 Cellular response to chemical stimulus
2.05e-23 101 Oxidation-reduction process
B 1.08e-09 17 Regulation of cell cycle process
1.16e-07 19 Regulation of organelle organization
1.86e-07 43 Oxidation-reduction process
8.83e-07 14 Regulation of mitotic cell cycle
8.83e-07 11 Regulation of nuclear division
1.07e-06 10 Regulation of mitotic nuclear division
1.09e-06 18 Regulation of cell cycle
2.09e-06 52 Phosphate-containing compound metabolic process
3.22e-06 52 Phosphorus metabolic process
3.22e-06 20 Regulation of cellular component organization
C 5.41e-28 60 Response to external stimulus
1.26e-27 51 Response to external biotic stimulus
1.26e-27 51 Response to other organism
1.81e-27 51 Response to biotic stimulus
1.94e-25 52 Defense response
4.73e-22 52 Multi-organism process
2.05e-19 52 Cellular response to chemical stimulus
4.83e-18 26 Response to fungus
2.46e-17 35 Defense response to other organism
3.99e-15 55 Response to abiotic stimulus
D 1.07e-27 124 Response to abiotic stimulus
4.30e-22 109 Response to organic substance
5.44e-21 94 Response to oxygen-containing compound
5.90e-19 33 Cellular response to decreased oxygen levels
5.90e-19 33 Cellular response to oxygen levels
5.90e-19 33 Cellular response to hypoxia
1.11e-18 81 Response to external stimulus
1.98e-18 86 Cellular response to chemical stimulus
1.52e-17 33 Response to hypoxia
2.22e-17 33 Response to decreased oxygen levels